44 citations · 71 across the 20 of their papers we have counts for
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cs.LG2023
Understanding and Improving Deep Graph Neural Networks: A Probabilistic Graphical Model Perspective
Jiayuan Chen, Xiang Zhang, Yinfei Xu +3
Recently, graph-based models designed for downstream tasks have significantly advanced research on graph neural networks (GNNs). GNN baselines based on neural message-passing mecha…
cs.LG2021★ 2 cited
Robust Graph Learning Under Wasserstein Uncertainty
Xiang Zhang, Yinfei Xu, Qinghe Liu +3
Graphs are playing a crucial role in different fields since they are powerful tools to unveil intrinsic relationships among signals. In many scenarios, an accurate graph structure…